文件 AWS 開發套件範例 GitHub 儲存庫中有更多可用的 [AWS SDK 範例](https://github.com/awsdocs/aws-doc-sdk-examples)。
本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
# Amazon EMR AWS SDKs的基本範例
下列程式碼範例示範如何搭配 AWS SDK 使用 Amazon EMR 的基本功能。
**Contents**
+ [動作](emr_code_examples_actions.md)
+ [`AddJobFlowSteps`](emr_example_emr_AddJobFlowSteps_section.md)
+ [`DescribeCluster`](emr_example_emr_DescribeCluster_section.md)
+ [`DescribeStep`](emr_example_emr_DescribeStep_section.md)
+ [`ListSteps`](emr_example_emr_ListSteps_section.md)
+ [`RunJobFlow`](emr_example_emr_RunJobFlow_section.md)
+ [`TerminateJobFlows`](emr_example_emr_TerminateJobFlows_section.md)
# Amazon EMR AWS SDKs的動作
下列程式碼範例示範如何使用 AWS SDKs 執行個別 Amazon EMR 動作。每個範例均包含 GitHub 的連結，您可以在連結中找到設定和執行程式碼的相關說明。
這些摘錄會呼叫 Amazon EMR API，是必須在內容中執行之大型程式的程式碼摘錄。您可以在 [使用 AWS SDKs Amazon EMR 案例](emr_code_examples_scenarios.md) 中查看內容中的動作。
下列範例僅包含最常使用的動作。如需完整清單，請參閱《[Amazon EMR API 參考](https://docs.aws.amazon.com/emr/latest/APIReference/Welcome.html)》。
**Topics**
+ [`AddJobFlowSteps`](emr_example_emr_AddJobFlowSteps_section.md)
+ [`DescribeCluster`](emr_example_emr_DescribeCluster_section.md)
+ [`DescribeStep`](emr_example_emr_DescribeStep_section.md)
+ [`ListSteps`](emr_example_emr_ListSteps_section.md)
+ [`RunJobFlow`](emr_example_emr_RunJobFlow_section.md)
+ [`TerminateJobFlows`](emr_example_emr_TerminateJobFlows_section.md)
# `AddJobFlowSteps` 搭配 AWS SDK 使用
下列程式碼範例示範如何使用 `AddJobFlowSteps`。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples)中設定和執行。
新增 Spark 步驟，該步驟會在新增叢集後立即執行。
```
def add_step(cluster_id, name, script_uri, script_args, emr_client):
"""
Adds a job step to the specified cluster. This example adds a Spark
step, which is run by the cluster as soon as it is added.
:param cluster_id: The ID of the cluster.
:param name: The name of the step.
:param script_uri: The URI where the Python script is stored.
:param script_args: Arguments to pass to the Python script.
:param emr_client: The Boto3 EMR client object.
:return: The ID of the newly added step.
"""
try:
response = emr_client.add_job_flow_steps(
JobFlowId=cluster_id,
Steps=[
{
"Name": name,
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": [
"spark-submit",
"--deploy-mode",
"cluster",
script_uri,
*script_args,
],
},
}
],
)
step_id = response["StepIds"][0]
logger.info("Started step with ID %s", step_id)
except ClientError:
logger.exception("Couldn't start step %s with URI %s.", name, script_uri)
raise
else:
return step_id
```
在叢集上執行 Amazon EMR 檔案系統 (EMRFS) 命令，以做為任務步驟。這可用於自動化叢集上的 EMRFS 命令，而不是透過 SSH 連線手動執行命令。
```
import boto3
from botocore.exceptions import ClientError
def add_emrfs_step(command, bucket_url, cluster_id, emr_client):
"""
Add an EMRFS command as a job flow step to an existing cluster.
:param command: The EMRFS command to run.
:param bucket_url: The URL of a bucket that contains tracking metadata.
:param cluster_id: The ID of the cluster to update.
:param emr_client: The Boto3 Amazon EMR client object.
:return: The ID of the added job flow step. Status can be tracked by calling
the emr_client.describe_step() function.
"""
job_flow_step = {
"Name": "Example EMRFS Command Step",
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": ["/usr/bin/emrfs", command, bucket_url],
},
}
try:
response = emr_client.add_job_flow_steps(
JobFlowId=cluster_id, Steps=[job_flow_step]
)
step_id = response["StepIds"][0]
print(f"Added step {step_id} to cluster {cluster_id}.")
except ClientError:
print(f"Couldn't add a step to cluster {cluster_id}.")
raise
else:
return step_id
def usage_demo():
emr_client = boto3.client("emr")
# Assumes the first waiting cluster has EMRFS enabled and has created metadata
# with the default name of 'EmrFSMetadata'.
cluster = emr_client.list_clusters(ClusterStates=["WAITING"])["Clusters"][0]
add_emrfs_step(
"sync", "s3://elasticmapreduce/samples/cloudfront", cluster["Id"], emr_client
)
if __name__ == "__main__":
usage_demo()
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [AddJobFlowSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/AddJobFlowSteps)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples)中設定和執行。
```
TRY.
" Build args list for Spark submit
DATA lt_args TYPE /aws1/cl_emrxmlstringlist_w=>tt_xmlstringlist.
APPEND NEW /aws1/cl_emrxmlstringlist_w( 'spark-submit' ) TO lt_args.
APPEND NEW /aws1/cl_emrxmlstringlist_w( '--deploy-mode' ) TO lt_args.
APPEND NEW /aws1/cl_emrxmlstringlist_w( 'cluster' ) TO lt_args.
APPEND NEW /aws1/cl_emrxmlstringlist_w( iv_script_uri ) TO lt_args.
APPEND LINES OF it_script_args TO lt_args.
" Create step configuration
DATA(lo_hadoop_jar_step) = NEW /aws1/cl_emrhadoopjarstepcfg(
iv_jar = 'command-runner.jar'
it_args = lt_args
).
DATA(lo_step_config) = NEW /aws1/cl_emrstepconfig(
iv_name = iv_name
iv_actiononfailure = 'CONTINUE'
io_hadoopjarstep = lo_hadoop_jar_step
).
DATA lt_steps TYPE /aws1/cl_emrstepconfig=>tt_stepconfiglist.
APPEND lo_step_config TO lt_steps.
DATA(lo_result) = lo_emr->addjobflowsteps(
iv_jobflowid = iv_cluster_id
it_steps = lt_steps
).
" Get first step ID
DATA(lt_step_ids) = lo_result->get_stepids( ).
READ TABLE lt_step_ids INDEX 1 INTO DATA(lo_step_id_obj).
IF sy-subrc = 0.
ov_step_id = lo_step_id_obj->get_value( ).
MESSAGE |Step added with ID { ov_step_id }| TYPE 'I'.
ENDIF.
CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [AddJobFlowSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `DescribeCluster` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `DescribeCluster`。
------
#### [ CLI ]
**AWS CLI**
命令：
```
aws emr describe-cluster --cluster-id j-XXXXXXXX
```
輸出：
```
For release-label based uniform instance groups cluster:
{
"Cluster": {
"Status": {
"Timeline": {
"ReadyDateTime": 1436475075.199,
"CreationDateTime": 1436474656.563,
},
"State": "WAITING",
"StateChangeReason": {
"Message": "Waiting for steps to run"
}
},
"Ec2InstanceAttributes": {
"ServiceAccessSecurityGroup": "sg-xxxxxxxx",
"EmrManagedMasterSecurityGroup": "sg-xxxxxxxx",
"IamInstanceProfile": "EMR_EC2_DefaultRole",
"Ec2KeyName": "myKey",
"Ec2AvailabilityZone": "us-east-1c",
"EmrManagedSlaveSecurityGroup": "sg-yyyyyyyyy"
},
"Name": "My Cluster",
"ServiceRole": "EMR_DefaultRole",
"Tags": [],
"TerminationProtected": true,
"UnhealthyNodeReplacement": true,
"ReleaseLabel": "emr-4.0.0",
"NormalizedInstanceHours": 96,
"InstanceGroups": [
{
"RequestedInstanceCount": 2,
"Status": {
"Timeline": {
"ReadyDateTime": 1436475074.245,
"CreationDateTime": 1436474656.564,
"EndDateTime": 1436638158.387
},
"State": "RUNNING",
"StateChangeReason": {
"Message": "",
}
},
"Name": "CORE",
"InstanceGroupType": "CORE",
"Id": "ig-YYYYYYY",
"Configurations": [],
"InstanceType": "m3.large",
"Market": "ON_DEMAND",
"RunningInstanceCount": 2
},
{
"RequestedInstanceCount": 1,
"Status": {
"Timeline": {
"ReadyDateTime": 1436475074.245,
"CreationDateTime": 1436474656.564,
"EndDateTime": 1436638158.387
},
"State": "RUNNING",
"StateChangeReason": {
"Message": "",
}
},
"Name": "MASTER",
"InstanceGroupType": "MASTER",
"Id": "ig-XXXXXXXXX",
"Configurations": [],
"InstanceType": "m3.large",
"Market": "ON_DEMAND",
"RunningInstanceCount": 1
}
],
"Applications": [
{
"Name": "Hadoop"
}
],
"VisibleToAllUsers": true,
"BootstrapActions": [],
"MasterPublicDnsName": "ec2-54-147-144-78.compute-1.amazonaws.com",
"AutoTerminate": false,
"Id": "j-XXXXXXXX",
"Configurations": [
{
"Properties": {
"fs.s3.consistent.retryPeriodSeconds": "20",
"fs.s3.enableServerSideEncryption": "true",
"fs.s3.consistent": "false",
"fs.s3.consistent.retryCount": "2"
},
"Classification": "emrfs-site"
}
]
}
}
For release-label based instance fleet cluster:
{
"Cluster": {
"Status": {
"Timeline": {
"ReadyDateTime": 1487897289.705,
"CreationDateTime": 1487896933.942
},
"State": "WAITING",
"StateChangeReason": {
"Message": "Waiting for steps to run"
}
},
"Ec2InstanceAttributes": {
"EmrManagedMasterSecurityGroup": "sg-xxxxx",
"RequestedEc2AvailabilityZones": [],
"RequestedEc2SubnetIds": [],
"IamInstanceProfile": "EMR_EC2_DefaultRole",
"Ec2AvailabilityZone": "us-east-1a",
"EmrManagedSlaveSecurityGroup": "sg-xxxxx"
},
"Name": "My Cluster",
"ServiceRole": "EMR_DefaultRole",
"Tags": [],
"TerminationProtected": false,
"UnhealthyNodeReplacement": false,
"ReleaseLabel": "emr-5.2.0",
"NormalizedInstanceHours": 472,
"InstanceCollectionType": "INSTANCE_FLEET",
"InstanceFleets": [
{
"Status": {
"Timeline": {
"ReadyDateTime": 1487897212.74,
"CreationDateTime": 1487896933.948
},
"State": "RUNNING",
"StateChangeReason": {
"Message": ""
}
},
"ProvisionedSpotCapacity": 1,
"Name": "MASTER",
"InstanceFleetType": "MASTER",
"LaunchSpecifications": {
"SpotSpecification": {
"TimeoutDurationMinutes": 60,
"TimeoutAction": "TERMINATE_CLUSTER"
}
},
"TargetSpotCapacity": 1,
"ProvisionedOnDemandCapacity": 0,
"InstanceTypeSpecifications": [
{
"BidPrice": "0.5",
"InstanceType": "m3.xlarge",
"WeightedCapacity": 1
}
],
"Id": "if-xxxxxxx",
"TargetOnDemandCapacity": 0
}
],
"Applications": [
{
"Version": "2.7.3",
"Name": "Hadoop"
}
],
"ScaleDownBehavior": "TERMINATE_AT_INSTANCE_HOUR",
"VisibleToAllUsers": true,
"BootstrapActions": [],
"MasterPublicDnsName": "ec2-xxx-xx-xxx-xx.compute-1.amazonaws.com",
"AutoTerminate": false,
"Id": "j-xxxxx",
"Configurations": []
}
}
For ami based uniform instance group cluster:
{
"Cluster": {
"Status": {
"Timeline": {
"ReadyDateTime": 1399400564.432,
"CreationDateTime": 1399400268.62
},
"State": "WAITING",
"StateChangeReason": {
"Message": "Waiting for steps to run"
}
},
"Ec2InstanceAttributes": {
"IamInstanceProfile": "EMR_EC2_DefaultRole",
"Ec2AvailabilityZone": "us-east-1c"
},
"Name": "My Cluster",
"Tags": [],
"TerminationProtected": true,
"UnhealthyNodeReplacement": true,
"RunningAmiVersion": "2.5.4",
"InstanceGroups": [
{
"RequestedInstanceCount": 1,
"Status": {
"Timeline": {
"ReadyDateTime": 1399400558.848,
"CreationDateTime": 1399400268.621
},
"State": "RUNNING",
"StateChangeReason": {
"Message": ""
}
},
"Name": "Master instance group",
"InstanceGroupType": "MASTER",
"InstanceType": "m1.small",
"Id": "ig-ABCD",
"Market": "ON_DEMAND",
"RunningInstanceCount": 1
},
{
"RequestedInstanceCount": 2,
"Status": {
"Timeline": {
"ReadyDateTime": 1399400564.439,
"CreationDateTime": 1399400268.621
},
"State": "RUNNING",
"StateChangeReason": {
"Message": ""
}
},
"Name": "Core instance group",
"InstanceGroupType": "CORE",
"InstanceType": "m1.small",
"Id": "ig-DEF",
"Market": "ON_DEMAND",
"RunningInstanceCount": 2
}
],
"Applications": [
{
"Version": "1.0.3",
"Name": "hadoop"
}
],
"BootstrapActions": [],
"VisibleToAllUsers": false,
"RequestedAmiVersion": "2.4.2",
"LogUri": "s3://myLogUri/",
"AutoTerminate": false,
"Id": "j-XXXXXXXX"
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DescribeCluster](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-cluster.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples)中設定和執行。
```
def describe_cluster(cluster_id, emr_client):
"""
Gets detailed information about a cluster.
:param cluster_id: The ID of the cluster to describe.
:param emr_client: The Boto3 EMR client object.
:return: The retrieved cluster information.
"""
try:
response = emr_client.describe_cluster(ClusterId=cluster_id)
cluster = response["Cluster"]
logger.info("Got data for cluster %s.", cluster["Name"])
except ClientError:
logger.exception("Couldn't get data for cluster %s.", cluster_id)
raise
else:
return cluster
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [DescribeCluster](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeCluster)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples)中設定和執行。
```
TRY.
oo_result = lo_emr->describecluster(
iv_clusterid = iv_cluster_id
).
DATA(lo_cluster) = oo_result->get_cluster( ).
DATA(lv_cluster_name) = lo_cluster->get_name( ).
MESSAGE |Retrieved cluster information for { lv_cluster_name }| TYPE 'I'.
CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
lv_error = lo_invalid_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [DescribeCluster](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `DescribeStep` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `DescribeStep`。
------
#### [ CLI ]
**AWS CLI**
下列命令描述叢集中步驟 ID 為 `s-3LZC0QUT43AM`，且叢集 ID 為 `j-3SD91U2E1L2QX` 的步驟：
```
aws emr describe-step --cluster-id j-3SD91U2E1L2QX --step-id s-3LZC0QUT43AM
```
輸出：
```
{
"Step": {
"Status": {
"Timeline": {
"EndDateTime": 1433200470.481,
"CreationDateTime": 1433199926.597,
"StartDateTime": 1433200404.959
},
"State": "COMPLETED",
"StateChangeReason": {}
},
"Config": {
"Args": [
"s3://us-west-2.elasticmapreduce/libs/hive/hive-script",
"--base-path",
"s3://us-west-2.elasticmapreduce/libs/hive/",
"--install-hive",
"--hive-versions",
"0.13.1"
],
"Jar": "s3://us-west-2.elasticmapreduce/libs/script-runner/script-runner.jar",
"Properties": {}
},
"Id": "s-3LZC0QUT43AM",
"ActionOnFailure": "TERMINATE_CLUSTER",
"Name": "Setup hive"
}
}
```
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DescribeStep](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-step.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples)中設定和執行。
```
def describe_step(cluster_id, step_id, emr_client):
"""
Gets detailed information about the specified step, including the current state of
the step.
:param cluster_id: The ID of the cluster.
:param step_id: The ID of the step.
:param emr_client: The Boto3 EMR client object.
:return: The retrieved information about the specified step.
"""
try:
response = emr_client.describe_step(ClusterId=cluster_id, StepId=step_id)
step = response["Step"]
logger.info("Got data for step %s.", step_id)
except ClientError:
logger.exception("Couldn't get data for step %s.", step_id)
raise
else:
return step
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [DescribeStep](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeStep)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples)中設定和執行。
```
TRY.
oo_result = lo_emr->describestep(
iv_clusterid = iv_cluster_id
iv_stepid = iv_step_id
).
DATA(lo_step) = oo_result->get_step( ).
DATA(lv_step_name) = lo_step->get_name( ).
MESSAGE |Retrieved step information for { lv_step_name }| TYPE 'I'.
CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
lv_error = lo_invalid_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [DescribeStep](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `ListSteps` 搭配 AWS SDK 或 CLI 使用
下列程式碼範例示範如何使用 `ListSteps`。
------
#### [ CLI ]
**AWS CLI**
下列命令會列出叢集中叢集 ID 為 `j-3SD91U2E1L2QX` 的所有步驟：
```
aws emr list-steps --cluster-id j-3SD91U2E1L2QX
```
+ 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [ListSteps](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/list-steps.html)。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples)中設定和執行。
```
def list_steps(cluster_id, emr_client):
"""
Gets a list of steps for the specified cluster. In this example, all steps are
returned, including completed and failed steps.
:param cluster_id: The ID of the cluster.
:param emr_client: The Boto3 EMR client object.
:return: The list of steps for the specified cluster.
"""
try:
response = emr_client.list_steps(ClusterId=cluster_id)
steps = response["Steps"]
logger.info("Got %s steps for cluster %s.", len(steps), cluster_id)
except ClientError:
logger.exception("Couldn't get steps for cluster %s.", cluster_id)
raise
else:
return steps
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [ListSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/ListSteps)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples)中設定和執行。
```
TRY.
oo_result = lo_emr->liststeps(
iv_clusterid = iv_cluster_id
).
DATA(lt_steps) = oo_result->get_steps( ).
DATA(lv_step_count) = lines( lt_steps ).
MESSAGE |Retrieved { lv_step_count } steps for cluster| TYPE 'I'.
CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
lv_error = lo_invalid_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [ListSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `RunJobFlow` 搭配 AWS SDK 使用
下列程式碼範例示範如何使用 `RunJobFlow`。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples)中設定和執行。
```
def run_job_flow(
name,
log_uri,
keep_alive,
applications,
job_flow_role,
service_role,
security_groups,
steps,
emr_client,
):
"""
Runs a job flow with the specified steps. A job flow creates a cluster of
instances and adds steps to be run on the cluster. Steps added to the cluster
are run as soon as the cluster is ready.
This example uses the 'emr-5.30.1' release. A list of recent releases can be
found here:
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-release-components.html.
:param name: The name of the cluster.
:param log_uri: The URI where logs are stored. This can be an Amazon S3 bucket URL,
such as 's3://my-log-bucket'.
:param keep_alive: When True, the cluster is put into a Waiting state after all
steps are run. When False, the cluster terminates itself when
the step queue is empty.
:param applications: The applications to install on each instance in the cluster,
such as Hive or Spark.
:param job_flow_role: The IAM role assumed by the cluster.
:param service_role: The IAM role assumed by the service.
:param security_groups: The security groups to assign to the cluster instances.
Amazon EMR adds all needed rules to these groups, so
they can be empty if you require only the default rules.
:param steps: The job flow steps to add to the cluster. These are run in order
when the cluster is ready.
:param emr_client: The Boto3 EMR client object.
:return: The ID of the newly created cluster.
"""
try:
response = emr_client.run_job_flow(
Name=name,
LogUri=log_uri,
ReleaseLabel="emr-5.30.1",
Instances={
"MasterInstanceType": "m5.xlarge",
"SlaveInstanceType": "m5.xlarge",
"InstanceCount": 3,
"KeepJobFlowAliveWhenNoSteps": keep_alive,
"EmrManagedMasterSecurityGroup": security_groups["manager"].id,
"EmrManagedSlaveSecurityGroup": security_groups["worker"].id,
},
Steps=[
{
"Name": step["name"],
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": [
"spark-submit",
"--deploy-mode",
"cluster",
step["script_uri"],
*step["script_args"],
],
},
}
for step in steps
],
Applications=[{"Name": app} for app in applications],
JobFlowRole=job_flow_role.name,
ServiceRole=service_role.name,
EbsRootVolumeSize=10,
VisibleToAllUsers=True,
)
cluster_id = response["JobFlowId"]
logger.info("Created cluster %s.", cluster_id)
except ClientError:
logger.exception("Couldn't create cluster.")
raise
else:
return cluster_id
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [RunJobFlow](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/RunJobFlow)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples)中設定和執行。
```
TRY.
" Create instances configuration
DATA(lo_instances) = NEW /aws1/cl_emrjobflowinstsconfig(
iv_masterinstancetype = 'm5.xlarge'
iv_slaveinstancetype = 'm5.xlarge'
iv_instancecount = 3
iv_keepjobflowalivewhennos00 = iv_keep_alive
iv_emrmanagedmastersecgroup = iv_primary_sec_grp
iv_emrmanagedslavesecgroup = iv_secondary_sec_grp
).
DATA(lo_result) = lo_emr->runjobflow(
iv_name = iv_name
iv_loguri = iv_log_uri
iv_releaselabel = 'emr-5.30.1'
io_instances = lo_instances
it_steps = it_steps
it_applications = it_applications
iv_jobflowrole = iv_job_flow_role
iv_servicerole = iv_service_role
iv_ebsrootvolumesize = 10
iv_visibletoallusers = abap_true
).
ov_cluster_id = lo_result->get_jobflowid( ).
MESSAGE 'EMR cluster created successfully.' TYPE 'I'.
CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrclientexc INTO DATA(lo_client_error).
lv_error = lo_client_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [RunJobFlow](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------
# `TerminateJobFlows` 搭配 AWS SDK 使用
下列程式碼範例示範如何使用 `TerminateJobFlows`。
------
#### [ Python ]
**適用於 Python 的 SDK (Boto3)**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples)中設定和執行。
```
def terminate_cluster(cluster_id, emr_client):
"""
Terminates a cluster. This terminates all instances in the cluster and cannot
be undone. Any data not saved elsewhere, such as in an Amazon S3 bucket, is lost.
:param cluster_id: The ID of the cluster to terminate.
:param emr_client: The Boto3 EMR client object.
"""
try:
emr_client.terminate_job_flows(JobFlowIds=[cluster_id])
logger.info("Terminated cluster %s.", cluster_id)
except ClientError:
logger.exception("Couldn't terminate cluster %s.", cluster_id)
raise
```
+ 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [TerminateJobFlows](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/TerminateJobFlows)。
------
#### [ SAP ABAP ]
**適用於 SAP ABAP 的開發套件**
GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples)中設定和執行。
```
TRY.
DATA lt_cluster_ids TYPE /aws1/cl_emrxmlstringlist_w=>tt_xmlstringlist.
APPEND NEW /aws1/cl_emrxmlstringlist_w( iv_cluster_id ) TO lt_cluster_ids.
lo_emr->terminatejobflows(
it_jobflowids = lt_cluster_ids
).
MESSAGE 'EMR cluster terminated successfully.' TYPE 'I'.
CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ 如需 API 詳細資訊，請參閱《適用於 *AWS SAP ABAP 的 SDK API 參考*》中的 [TerminateJobFlows](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。
------